Objective:To analyze the effects of shared decision-making in patients with type 2 diabetic mellitus.Methods:Databases including PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang Datebase, COVIP and SinoMed, for randomized controlled trials (RCTs) on the application of shared decision-making in patients with type 2 diabetic mellitus from inception to July 22, 2023, used R Studio software for meta-analysis.Results:A total of 14 RCTs and 2 606 patients with type 2 diabetic mellitus were included. The results of meta-analysis showed that the shared decision-making can alleviate the decision-making conflict of type 2 diabetic mellitus patients ( MD=-3.18, 95% CI -5.36 to -0.99, P<0.05), improve the decision-making self-efficacy ( MD=5.82, 95% CI 2.34 to 9.30, P<0.05), medication compliance ( RR=1.08, 95% CI 1.01 to 1.16, P<0.05), and diabetes-related knowledge ( SMD=0.46, 95% CI 0.16 to 0.75, P<0.05), reduce BMI ( MD=-0.75, 95% CI-1.33 to -0.17, P<0.05) and the HbA1c level ( MD=-0.45, 95% CI -0.65 to -0.24, P<0.05) in the 3-month follow-up. Conclusions:The shared decision-making improves the self-management in patients with type 2 diabetic mellitus. However, the long-term effect and potential risks of this model still need to be further studied. It is suggested that the application of shared decision-making in type 2 diabetic mellitus patients should be optimized in the future, and research on the long-term effects and potential risks of this model should be increased.

Objective:To analyze the effects of shared decision-making in patients with type 2 diabetic mellitus.Methods:Databases including PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wanfang Datebase, COVIP and SinoMed, for randomized controlled trials (RCTs) on the application of shared decision-making in patients with type 2 diabetic mellitus from inception to July 22, 2023, used R Studio software for meta-analysis.Results:A total of 14 RCTs and 2 606 patients with type 2 diabetic mellitus were included. The results of meta-analysis showed that the shared decision-making can alleviate the decision-making conflict of type 2 diabetic mellitus patients ( MD=-3.18, 95% CI -5.36 to -0.99, P<0.05), improve the decision-making self-efficacy ( MD=5.82, 95% CI 2.34 to 9.30, P<0.05), medication compliance ( RR=1.08, 95% CI 1.01 to 1.16, P<0.05), and diabetes-related knowledge ( SMD=0.46, 95% CI 0.16 to 0.75, P<0.05), reduce BMI ( MD=-0.75, 95% CI-1.33 to -0.17, P<0.05) and the HbA1c level ( MD=-0.45, 95% CI -0.65 to -0.24, P<0.05) in the 3-month follow-up. Conclusions:The shared decision-making improves the self-management in patients with type 2 diabetic mellitus. However, the long-term effect and potential risks of this model still need to be further studied. It is suggested that the application of shared decision-making in type 2 diabetic mellitus patients should be optimized in the future, and research on the long-term effects and potential risks of this model should be increased.

Objective:To systematically evaluate the risk of bias and applicability of risk prediction models for the progression of diabetic nephropathy (DN) .Methods:A systematic search was conducted in CNKI, CBMdisc, Wanfang, VIP, PubMed, Web of Science, Embase, and CINAHL for literature related to DN progression prediction models, with a search timeline up to April 30, 2023. Two researchers independently screened the literature and extracted data according to a checklist for key assessments of prediction model studies and the PROBAST tool for assessing risk of bias in prediction models.Results:A total of nine articles encompassing 15 models were included. Of these, eight studies were retrospective study, and one was a randomized controlled trial. The area under the receiver operating characteristic curve ( AUC) for these models ranged from 0.626 to 0.986. Three studies conducted external validation, and seven studies conducted internal validation. Commonly repeated predictive factors included eGFR, cystatin C, and glycated hemoglobin (HbA1c). While the overall applicability of the models was good, methodological issues such as inappropriate data acquisition, selection of predictive factors, and neglect of model performance evaluation contributed to a certain risk of bias. Conclusions:The current DN progression risk prediction models demonstrate good discrimination and applicability. However, most models lack comprehensive calibration assessments and exhibit methodological flaws. Future research should focus on developing models with better applicability and lower bias, coupled with effective internal and external validation.